Artigos de revistas sobre o tema "Deep supervised learning"
Crie uma referência precisa em APA, MLA, Chicago, Harvard, e outros estilos
Veja os 50 melhores artigos de revistas para estudos sobre o assunto "Deep supervised learning".
Ao lado de cada fonte na lista de referências, há um botão "Adicionar à bibliografia". Clique e geraremos automaticamente a citação bibliográfica do trabalho escolhido no estilo de citação de que você precisa: APA, MLA, Harvard, Chicago, Vancouver, etc.
Você também pode baixar o texto completo da publicação científica em formato .pdf e ler o resumo do trabalho online se estiver presente nos metadados.
Veja os artigos de revistas das mais diversas áreas científicas e compile uma bibliografia correta.
Kim, Taeheon, Jaewon Hur e Youkyung Han. "Very High-Resolution Satellite Image Registration Based on Self-supervised Deep Learning". Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography 41, n.º 4 (31 de agosto de 2023): 217–25. http://dx.doi.org/10.7848/ksgpc.2023.41.4.217.
Texto completo da fonteAlZuhair, Mona Suliman, Mohamed Maher Ben Ismail e Ouiem Bchir. "Soft Semi-Supervised Deep Learning-Based Clustering". Applied Sciences 13, n.º 17 (27 de agosto de 2023): 9673. http://dx.doi.org/10.3390/app13179673.
Texto completo da fonteWei, Xiang, Xiaotao Wei, Xiangyuan Kong, Siyang Lu, Weiwei Xing e Wei Lu. "FMixCutMatch for semi-supervised deep learning". Neural Networks 133 (janeiro de 2021): 166–76. http://dx.doi.org/10.1016/j.neunet.2020.10.018.
Texto completo da fonteZhou, Shusen, Hailin Zou, Chanjuan Liu, Mujun Zang, Zhiwang Zhang e Jun Yue. "Deep extractive networks for supervised learning". Optik 127, n.º 20 (outubro de 2016): 9008–19. http://dx.doi.org/10.1016/j.ijleo.2016.07.007.
Texto completo da fonteFong, A. C. M., e G. Hong. "Boosted Supervised Intensional Learning Supported by Unsupervised Learning". International Journal of Machine Learning and Computing 11, n.º 2 (março de 2021): 98–102. http://dx.doi.org/10.18178/ijmlc.2021.11.2.1020.
Texto completo da fonteHu, Yu, e Hongmin Cai. "Hypergraph-Supervised Deep Subspace Clustering". Mathematics 9, n.º 24 (15 de dezembro de 2021): 3259. http://dx.doi.org/10.3390/math9243259.
Texto completo da fonteFu, Zheren, Yan Li, Zhendong Mao, Quan Wang e Yongdong Zhang. "Deep Metric Learning with Self-Supervised Ranking". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 2 (18 de maio de 2021): 1370–78. http://dx.doi.org/10.1609/aaai.v35i2.16226.
Texto completo da fonteDutta, Ujjal Kr, Mehrtash Harandi e C. Chandra Shekhar. "Semi-Supervised Metric Learning: A Deep Resurrection". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 8 (18 de maio de 2021): 7279–87. http://dx.doi.org/10.1609/aaai.v35i8.16894.
Texto completo da fonteBharati, Aparna, Richa Singh, Mayank Vatsa e Kevin W. Bowyer. "Detecting Facial Retouching Using Supervised Deep Learning". IEEE Transactions on Information Forensics and Security 11, n.º 9 (setembro de 2016): 1903–13. http://dx.doi.org/10.1109/tifs.2016.2561898.
Texto completo da fonteMathilde Caron. "Self-supervised learning of deep visual representations". Bulletin 1024, n.º 21 (abril de 2023): 171–72. http://dx.doi.org/10.48556/sif.1024.21.171.
Texto completo da fonteQin, Shanshan, Nayantara Mudur e Cengiz Pehlevan. "Contrastive Similarity Matching for Supervised Learning". Neural Computation 33, n.º 5 (13 de abril de 2021): 1300–1328. http://dx.doi.org/10.1162/neco_a_01374.
Texto completo da fonteAlzahrani, Theiab, Baidaa Al-Bander e Waleed Al-Nuaimy. "Deep Learning Models for Automatic Makeup Detection". AI 2, n.º 4 (14 de outubro de 2021): 497–511. http://dx.doi.org/10.3390/ai2040031.
Texto completo da fonteWu, Haiping, Khimya Khetarpal e Doina Precup. "Self-Supervised Attention-Aware Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 12 (18 de maio de 2021): 10311–19. http://dx.doi.org/10.1609/aaai.v35i12.17235.
Texto completo da fonteGupta, Jaya, Sunil Pathak e Gireesh Kumar. "Deep Learning (CNN) and Transfer Learning: A Review". Journal of Physics: Conference Series 2273, n.º 1 (1 de maio de 2022): 012029. http://dx.doi.org/10.1088/1742-6596/2273/1/012029.
Texto completo da fonteGupta, Jaya, Sunil Pathak e Gireesh Kumar. "Deep Learning (CNN) and Transfer Learning: A Review". Journal of Physics: Conference Series 2273, n.º 1 (1 de maio de 2022): 012029. http://dx.doi.org/10.1088/1742-6596/2273/1/012029.
Texto completo da fonteGupta, Ashwani, e Utpal Sharma. "Deep Learning-Based Aspect Term Extraction for Sentiment Analysis in Hindi". Indian Journal Of Science And Technology 17, n.º 7 (15 de fevereiro de 2024): 625–34. http://dx.doi.org/10.17485/ijst/v17i7.2766.
Texto completo da fonteKim, Chayoung. "Deep Q-Learning Network with Bayesian-Based Supervised Expert Learning". Symmetry 14, n.º 10 (13 de outubro de 2022): 2134. http://dx.doi.org/10.3390/sym14102134.
Texto completo da fonteLin, Yi-Nan, Tsang-Yen Hsieh, Cheng-Ying Yang, Victor RL Shen, Tony Tong-Ying Juang e Wen-Hao Chen. "Deep Petri nets of unsupervised and supervised learning". Measurement and Control 53, n.º 7-8 (9 de junho de 2020): 1267–77. http://dx.doi.org/10.1177/0020294020923375.
Texto completo da fonteYin, Chunwu, e Zhanbo Chen. "Developing Sustainable Classification of Diseases via Deep Learning and Semi-Supervised Learning". Healthcare 8, n.º 3 (24 de agosto de 2020): 291. http://dx.doi.org/10.3390/healthcare8030291.
Texto completo da fonteChong, De Wei, Kenny, e Abel Yang. "Photometric Redshift Analysis using Supervised Learning Algorithms and Deep Learning". EPJ Web of Conferences 206 (2019): 09006. http://dx.doi.org/10.1051/epjconf/201920609006.
Texto completo da fonteChen, Chong, Ying Liu, Maneesh Kumar, Jian Qin e Yunxia Ren. "Energy consumption modelling using deep learning embedded semi-supervised learning". Computers & Industrial Engineering 135 (setembro de 2019): 757–65. http://dx.doi.org/10.1016/j.cie.2019.06.052.
Texto completo da fonteLe, Linh, Ying Xie e Vijay V. Raghavan. "KNN Loss and Deep KNN". Fundamenta Informaticae 182, n.º 2 (30 de setembro de 2021): 95–110. http://dx.doi.org/10.3233/fi-2021-2068.
Texto completo da fonteGuo, Yuejun, Orhan Ermis, Qiang Tang, Hoang Trang e Alexandre De Oliveira. "An Empirical Study of Deep Learning-Based SS7 Attack Detection". Information 14, n.º 9 (16 de setembro de 2023): 509. http://dx.doi.org/10.3390/info14090509.
Texto completo da fonteNafea, Ahmed Adil, Saeed Amer Alameri, Russel R. Majeed, Meaad Ali Khalaf e Mohammed M. AL-Ani. "A Short Review on Supervised Machine Learning and Deep Learning Techniques in Computer Vision". Babylonian Journal of Machine Learning 2024 (11 de fevereiro de 2024): 48–55. http://dx.doi.org/10.58496/bjml/2024/004.
Texto completo da fonteLiu, MengYang, MingJun Li e XiaoYang Zhang. "The Application of the Unsupervised Migration Method Based on Deep Learning Model in the Marketing Oriented Allocation of High Level Accounting Talents". Computational Intelligence and Neuroscience 2022 (6 de junho de 2022): 1–10. http://dx.doi.org/10.1155/2022/5653942.
Texto completo da fonteLiu, MengYang, MingJun Li e XiaoYang Zhang. "The Application of the Unsupervised Migration Method Based on Deep Learning Model in the Marketing Oriented Allocation of High Level Accounting Talents". Computational Intelligence and Neuroscience 2022 (6 de junho de 2022): 1–10. http://dx.doi.org/10.1155/2022/5653942.
Texto completo da fonteShwartz Ziv, Ravid, e Yann LeCun. "To Compress or Not to Compress—Self-Supervised Learning and Information Theory: A Review". Entropy 26, n.º 3 (12 de março de 2024): 252. http://dx.doi.org/10.3390/e26030252.
Texto completo da fonteWang, Guo-Hua, e Jianxin Wu. "Repetitive Reprediction Deep Decipher for Semi-Supervised Learning". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 6170–77. http://dx.doi.org/10.1609/aaai.v34i04.6082.
Texto completo da fonteAugustine, Tanya N. "Weakly-supervised deep learning models in computational pathology". eBioMedicine 81 (julho de 2022): 104117. http://dx.doi.org/10.1016/j.ebiom.2022.104117.
Texto completo da fonteKang, Xudong, Binbin Zhuo e Puhong Duan. "Semi-supervised deep learning for hyperspectral image classification". Remote Sensing Letters 10, n.º 4 (3 de janeiro de 2019): 353–62. http://dx.doi.org/10.1080/2150704x.2018.1557787.
Texto completo da fonteAugusta, Carolyn, Rob Deardon e Graham Taylor. "Deep learning for supervised classification of spatial epidemics". Spatial and Spatio-temporal Epidemiology 29 (junho de 2019): 187–98. http://dx.doi.org/10.1016/j.sste.2018.08.002.
Texto completo da fonteZeng, Zeng, Yang Xulei, Yu Qiyun, Yao Meng e Zhang Le. "SeSe-Net: Self-Supervised deep learning for segmentation". Pattern Recognition Letters 128 (dezembro de 2019): 23–29. http://dx.doi.org/10.1016/j.patrec.2019.08.002.
Texto completo da fonteIto, Ryo, Ken Nakae, Junichi Hata, Hideyuki Okano e Shin Ishii. "Semi-supervised deep learning of brain tissue segmentation". Neural Networks 116 (agosto de 2019): 25–34. http://dx.doi.org/10.1016/j.neunet.2019.03.014.
Texto completo da fonteTang, Xin, Fang Guo, Jianbing Shen e Tianyuan Du. "Facial landmark detection by semi-supervised deep learning". Neurocomputing 297 (julho de 2018): 22–32. http://dx.doi.org/10.1016/j.neucom.2018.01.080.
Texto completo da fonteLi, Zhun, ByungSoo Ko e Ho-Jin Choi. "Naive semi-supervised deep learning using pseudo-label". Peer-to-Peer Networking and Applications 12, n.º 5 (10 de dezembro de 2018): 1358–68. http://dx.doi.org/10.1007/s12083-018-0702-9.
Texto completo da fonteXiang, Xuezhi, Mingliang Zhai, Rongfang Zhang, Yulong Qiao e Abdulmotaleb El Saddik. "Deep Optical Flow Supervised Learning With Prior Assumptions". IEEE Access 6 (2018): 43222–32. http://dx.doi.org/10.1109/access.2018.2863233.
Texto completo da fonteHu, Yaxian, Senlin Luo, Longfei Han, Limin Pan e Tiemei Zhang. "Deep supervised learning with mixture of neural networks". Artificial Intelligence in Medicine 102 (janeiro de 2020): 101764. http://dx.doi.org/10.1016/j.artmed.2019.101764.
Texto completo da fonteLingyi, Jiang, Zheng Yifeng, Chen Che, Li Guohe e Zhang Wenjie. "Review of optimization methods for supervised deep learning". Journal of Image and Graphics 28, n.º 4 (2023): 963–83. http://dx.doi.org/10.11834/jig.211139.
Texto completo da fonteHu, Peng, Liangli Zhen, Xi Peng, Hongyuan Zhu, Jie Lin, Xu Wang e Dezhong Peng. "Deep Supervised Multi-View Learning With Graph Priors". IEEE Transactions on Image Processing 33 (2024): 123–33. http://dx.doi.org/10.1109/tip.2023.3335825.
Texto completo da fonteWeikang, Xiang, Zhou Quan, Cui Jingcheng, Mo Zhiyi, Wu Xiaofu, Ou Weihua, Wang Jingdong e Liu Wenyu. "Weakly supervised semantic segmentation based on deep learning". Journal of Image and Graphics 29, n.º 5 (2024): 1146–68. http://dx.doi.org/10.11834/jig.230628.
Texto completo da fonteAversa, Rossella, Piero Coronica, Cristiano De Nobili e Stefano Cozzini. "Deep Learning, Feature Learning, and Clustering Analysis for SEM Image Classification". Data Intelligence 2, n.º 4 (outubro de 2020): 513–28. http://dx.doi.org/10.1162/dint_a_00062.
Texto completo da fonteEpstein, Sean C., Timothy J. P. Bray, Margaret Hall-Craggs e Hui Zhang. "Choice of training label matters: how to best use deep learning for quantitative MRI parameter estimation". Machine Learning for Biomedical Imaging 2, January 2024 (23 de janeiro de 2024): 586–610. http://dx.doi.org/10.59275/j.melba.2024-geb5.
Texto completo da fontePrashant Krishnan, V., S. Rajarajeswari, Venkat Krishnamohan, Vivek Chandra Sheel e R. Deepak. "Music Generation Using Deep Learning Techniques". Journal of Computational and Theoretical Nanoscience 17, n.º 9 (1 de julho de 2020): 3983–87. http://dx.doi.org/10.1166/jctn.2020.9003.
Texto completo da fonteZheng, Huan, Tongyao Pang e Hui Ji. "Unsupervised Deep Video Denoising with Untrained Network". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 3 (26 de junho de 2023): 3651–59. http://dx.doi.org/10.1609/aaai.v37i3.25476.
Texto completo da fonteSong, Jingkuan, Lianli Gao, Fuhao Zou, Yan Yan e Nicu Sebe. "Deep and fast: Deep learning hashing with semi-supervised graph construction". Image and Vision Computing 55 (novembro de 2016): 101–8. http://dx.doi.org/10.1016/j.imavis.2016.02.005.
Texto completo da fonteVanyan, Ani, e Hrant Khachatrian. "Deep Semi-Supervised Image Classification Algorithms: a Survey". JUCS - Journal of Universal Computer Science 27, n.º 12 (28 de dezembro de 2021): 1390–407. http://dx.doi.org/10.3897/jucs.77029.
Texto completo da fonteTekleselassie, Hailye. "A Deep Learning Approach for DDoS Attack Detection Using Supervised Learning". MATEC Web of Conferences 348 (2021): 01012. http://dx.doi.org/10.1051/matecconf/202134801012.
Texto completo da fonteAdke, Shrinidhi, Changying Li, Khaled M. Rasheed e Frederick W. Maier. "Supervised and Weakly Supervised Deep Learning for Segmentation and Counting of Cotton Bolls Using Proximal Imagery". Sensors 22, n.º 10 (12 de maio de 2022): 3688. http://dx.doi.org/10.3390/s22103688.
Texto completo da fonteLi, Ji, Yuesong Nan e Hui Ji. "Un-supervised learning for blind image deconvolution via Monte-Carlo sampling". Inverse Problems 38, n.º 3 (11 de fevereiro de 2022): 035012. http://dx.doi.org/10.1088/1361-6420/ac4ede.
Texto completo da fonteNisha.C.M e N. Thangarasu. "Deep learning algorithms and their relevance: A review". International Journal of Data Informatics and Intelligent Computing 2, n.º 4 (9 de dezembro de 2023): 1–10. http://dx.doi.org/10.59461/ijdiic.v2i4.78.
Texto completo da fonte